Post Roe Map¶

This analysis seeks to quantify the levels of harm caused / travel time and distance cost incurred in this new old world. Intuitively, someone seeking care in PA would have an easier time crossing state lines for care vs someone in LA. This attempts to wrap our heads around these inequities.

Full success could be creating a model to focus resources that reduce the net cost/harm incurred. E.g. while establishing protections in ID would be a good thing, it wouldn't help nearly as many people as establishing protections in TN.

If you're interested in executing the notebook/ playing with source: https://github.com/parquar/us-census-zips-geo/tree/dev/post_roe

In [ ]:
import pandas as pd
import plotly.express as px

from lib import  load_k_closest_clinic_distances
from helpers import zip3_census, draw_status_treemap

draw_status_treemap()
In [ ]:
# adds drive miles and drive time from google distance matrix api
closest_clinics_from_at_risk_origins = load_k_closest_clinic_distances()
closest_clinics_from_at_risk_origins
Out[ ]:
origin_lat origin_lng origin_state dest_lat dest_lng destination_state geodesic_miles drive_miles drive_duration _roundtrip_hours
0 34.315791 -85.295125 GA 37.528691 -88.453991 IL 283 380.0 365.0 12.166667
1 34.315791 -85.295125 GA 37.540668 -88.816118 IL 297 374.0 349.0 11.633333
2 34.315791 -85.295125 GA 37.139667 -89.333679 IL 299 386.0 376.0 12.533333
3 34.315791 -85.295125 GA 37.226148 -89.294708 IL 301 385.0 369.0 12.300000
4 34.315791 -85.295125 GA 37.476199 -89.032362 IL 302 377.0 356.0 11.866667
... ... ... ... ... ... ... ... ... ... ...
5513 43.639891 -110.731186 WY 42.854644 -117.509120 OR 346 488.0 466.0 15.533333
5514 43.639891 -110.731186 WY 40.045690 -105.200890 CO 377 478.0 474.0 15.800000
5515 43.639891 -110.731186 WY 39.671344 -116.013637 NV 386 527.0 504.0 16.800000
5516 43.639891 -110.731186 WY 40.428841 -116.961096 NV 389 509.0 499.0 16.633333
5517 43.639891 -110.731186 WY 38.972891 -106.380522 USA 393 543.0 551.0 18.366667

5518 rows × 10 columns

In [ ]:
def _get_distance_from_origin_stats(closest_clinics):
    # groups by orig, and finds aggregate stats on the 10 closest clinics
    distance_from_origin_stats = closest_clinics.groupby(['origin_state','origin_lat','origin_lng']).agg(
            k=("origin_lat","count"),
            one_way_crow_flies_mean=("geodesic_miles","mean"), 
            one_way_drive_miles_mean=("drive_miles","mean"),
            round_trip_drive_hrs_mean=("_roundtrip_hours","mean")
        ).reset_index()

    census_stats = zip3_census.rename(columns={"_state": "origin_state", "_lat": "origin_lat", "_lng": "origin_lng","_zip3":"origin_zip3"})
    distance_from_origin_stats = census_stats.merge(distance_from_origin_stats) # add population + adi 
    return distance_from_origin_stats

distance_from_origin_stats = _get_distance_from_origin_stats(closest_clinics_from_at_risk_origins)
distance_from_origin_stats
Out[ ]:
origin_state origin_zip3 origin_lat origin_lng _census_total _adi_mean k one_way_crow_flies_mean one_way_drive_miles_mean round_trip_drive_hrs_mean
0 AL 350** 33.568065 -86.712692 442190 71.368421 10 316.4 401.6 12.230000
1 AL 351** 33.506417 -86.624839 324591 65.675000 10 322.3 412.6 12.630000
2 AL 352** 33.506077 -86.798758 491463 66.857143 10 318.5 395.8 12.096667
3 AL 354** 33.113778 -87.787003 225321 73.265625 10 328.3 446.4 13.920000
4 AL 355** 33.973542 -87.662356 158109 85.100000 10 272.4 366.9 12.033333
... ... ... ... ... ... ... ... ... ... ...
521 WY 823** 41.804052 -106.980921 15430 67.400000 10 168.0 231.0 7.973333
522 WY 824** 44.438020 -108.408179 52930 51.636364 10 349.0 492.4 15.533333
523 WY 825** 43.103802 -108.847958 38910 60.888889 10 273.8 366.8 12.453333
524 WY 828** 44.714826 -106.872537 36958 37.461538 10 351.8 447.8 13.426667
525 WY 830** 43.639891 -110.731186 20421 3.800000 10 342.8 454.9 15.386667

526 rows × 10 columns

In [ ]:
px.box(
    distance_from_origin_stats,
    x="origin_state",
    y="round_trip_drive_hrs_mean",
    title="Round trip driving hours required to access care | by State",
).show(renderer="notebook")
In [ ]:
n = len(distance_from_origin_stats)
round_trip_mean_of_means = distance_from_origin_stats['round_trip_drive_hrs_mean'].mean()
px.scatter_mapbox(
    distance_from_origin_stats,
    lat="origin_lat",
    lon="origin_lng",
    size_max=15,
    hover_data=["one_way_crow_flies_mean","one_way_drive_miles_mean","_adi_mean"],
    height=800,
    zoom=3,
    size="_census_total",
    color="round_trip_drive_hrs_mean",
    title=f"Unprotected Origin Locations | n={n} | scaled by Population | Color=avg round trip drive time | round_trip_mean={'{:.2f}'.format(round_trip_mean_of_means)} hours",
).show(renderer="notebook")
In [ ]:
px.histogram(
    distance_from_origin_stats, x='round_trip_drive_hrs_mean',
    title=f"On average, cares seekers would need to drive {'{:.2f}'.format(round_trip_mean_of_means)} hours round trip | from n={n} origins"
).show(renderer="notebook")

_workbench¶

In [ ]:
# total cost fn
from scipy.stats import zscore
df = distance_from_origin_stats[['_census_total','_adi_mean','round_trip_drive_hrs_mean']]
df
Out[ ]:
_census_total _adi_mean round_trip_drive_hrs_mean
0 442190 71.368421 12.230000
1 324591 65.675000 12.630000
2 491463 66.857143 12.096667
3 225321 73.265625 13.920000
4 158109 85.100000 12.033333
... ... ... ...
521 15430 67.400000 7.973333
522 52930 51.636364 15.533333
523 38910 60.888889 12.453333
524 36958 37.461538 13.426667
525 20421 3.800000 15.386667

526 rows × 3 columns